code: def test_attentionunet(self):
      for dims in [2, 3]:
          shape = (3, 1) + (92,) * dims
          input = torch.rand(*shape)
          model = att.AttentionUnet(
              spatial_dims=dims,
              in_channels=1,
              out_channels=2,
              channels=(3, 4, 5),
              up_kernel_size=5,
              strides=(1, 2),
          )
          output = model(input)
          self.assertEqual(output.shape[2:], input.shape[2:])
          self.assertEqual(output.shape[0], input.shape[0])
          self.assertEqual(output.shape[1], 2)
  
file_path: MONAI/tests/test_attentionunet.py
imports: from __future__ import annotations
  import unittest
  import torch
  import monai.networks.nets.attentionunet
  from tests.utils import skip_if_no_cuda, skip_if_quick
label_desc: Use image node_id 2 for calling the TestAttentionUnet obj's underlying member method code with example usage: obj.test_attentionunet() without return types